import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  A_			(							snake_case_			):
  _SCREAMING_SNAKE_CASE     					=      (IPNDMScheduler,)
  _SCREAMING_SNAKE_CASE     					=      (('num_inference_steps', 50),)

  def 		_UpperCAmelCase (		self			:   List[str]  ,				**__SCREAMING_SNAKE_CASE			:   Dict							):
   __a							=	{'''num_train_timesteps''': 10_00}
   config.update(**lowerCAmelCase__							)
   return config

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0  ,				**__SCREAMING_SNAKE_CASE			:   Dict							):
   __a							=	dict(self.forward_default_kwargs							)
   __a							=	kwargs.pop("num_inference_steps"  ,				lowerCAmelCase__							)
   __a							=	self.dummy_sample
   __a							=	0.1 * sample
   __a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
    __a							=	self.get_scheduler_config(**lowerCAmelCase__							)
    __a							=	scheduler_class(**lowerCAmelCase__							)
    scheduler.set_timesteps(lowerCAmelCase__							)
    # copy over dummy past residuals
    __a							=	dummy_past_residuals[:]

    if time_step is None:
     __a							=	scheduler.timesteps[len(scheduler.timesteps							) // 2]

    with tempfile.TemporaryDirectory() as tmpdirname:
     scheduler.save_config(lowerCAmelCase__							)
     __a							=	scheduler_class.from_pretrained(lowerCAmelCase__							)
     new_scheduler.set_timesteps(lowerCAmelCase__							)
     # copy over dummy past residuals
     __a							=	dummy_past_residuals[:]

    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample
    __a							=	new_scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample

    assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample
    __a							=	new_scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample

    assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   pass

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Any=0  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):
   __a							=	dict(self.forward_default_kwargs							)
   __a							=	kwargs.pop("num_inference_steps"  ,				lowerCAmelCase__							)
   __a							=	self.dummy_sample
   __a							=	0.1 * sample
   __a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
    __a							=	self.get_scheduler_config()
    __a							=	scheduler_class(**lowerCAmelCase__							)
    scheduler.set_timesteps(lowerCAmelCase__							)

    # copy over dummy past residuals (must be after setting timesteps)
    __a							=	dummy_past_residuals[:]

    if time_step is None:
     __a							=	scheduler.timesteps[len(scheduler.timesteps							) // 2]

    with tempfile.TemporaryDirectory() as tmpdirname:
     scheduler.save_config(lowerCAmelCase__							)
     __a							=	scheduler_class.from_pretrained(lowerCAmelCase__							)
     # copy over dummy past residuals
     new_scheduler.set_timesteps(lowerCAmelCase__							)

     # copy over dummy past residual (must be after setting timesteps)
     __a							=	dummy_past_residuals[:]

    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample
    __a							=	new_scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample

    assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample
    __a							=	new_scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample

    assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				**__SCREAMING_SNAKE_CASE			:   Any							):
   __a							=	self.scheduler_classes[0]
   __a							=	self.get_scheduler_config(**lowerCAmelCase__							)
   __a							=	scheduler_class(**lowerCAmelCase__							)

   __a							=	10
   __a							=	self.dummy_model()
   __a							=	self.dummy_sample_deter
   scheduler.set_timesteps(lowerCAmelCase__							)

   for i, t in enumerate(scheduler.timesteps							):
    __a							=	model(lowerCAmelCase__  ,				lowerCAmelCase__							)
    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__							).prev_sample

   for i, t in enumerate(scheduler.timesteps							):
    __a							=	model(lowerCAmelCase__  ,				lowerCAmelCase__							)
    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__							).prev_sample

   return sample

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	dict(self.forward_default_kwargs							)

   __a							=	kwargs.pop("num_inference_steps"  ,				lowerCAmelCase__							)

   for scheduler_class in self.scheduler_classes:
    __a							=	self.get_scheduler_config()
    __a							=	scheduler_class(**lowerCAmelCase__							)

    __a							=	self.dummy_sample
    __a							=	0.1 * sample

    if num_inference_steps is not None and hasattr(lowerCAmelCase__  ,				"set_timesteps"							):
     scheduler.set_timesteps(lowerCAmelCase__							)
    elif num_inference_steps is not None and not hasattr(lowerCAmelCase__  ,				"set_timesteps"							):
     __a							=	num_inference_steps

    # copy over dummy past residuals (must be done after set_timesteps)
    __a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
    __a							=	dummy_past_residuals[:]

    __a							=	scheduler.timesteps[5]
    __a							=	scheduler.timesteps[6]

    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample
    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample

    self.assertEqual(output_a.shape  ,				sample.shape							)
    self.assertEqual(output_a.shape  ,				output_a.shape							)

    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample
    __a							=	scheduler.step(lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				**lowerCAmelCase__							).prev_sample

    self.assertEqual(output_a.shape  ,				sample.shape							)
    self.assertEqual(output_a.shape  ,				output_a.shape							)

  def 		_UpperCAmelCase (		self			:   Any							):
   for timesteps in [1_00, 10_00]:
    self.check_over_configs(num_train_timesteps=lowerCAmelCase__  ,				time_step=lowerCAmelCase__							)

  def 		_UpperCAmelCase (		self			:   str							):
   for t, num_inference_steps in zip([1, 5, 10]  ,				[10, 50, 1_00]							):
    self.check_over_forward(num_inference_steps=lowerCAmelCase__  ,				time_step=lowerCAmelCase__							)

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	self.full_loop()
   __a							=	torch.mean(torch.abs(lowerCAmelCase__							)							)

   assert abs(result_mean.item() - 2_54_05_29							) < 10







'''simple docstring'''



import os

lowerCAmelCase_  :							Union[str, Any]             =    {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
def        UpperCAmelCase (    A	:					str					):
							SCREAMING_SNAKE_CASE       :   str         =      0

							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							while index < len(A					) - 1:
														SCREAMING_SNAKE_CASE       :   Dict         =      SYMBOLS[numerals[index]]
														SCREAMING_SNAKE_CASE       :   str         =      SYMBOLS[numerals[index + 1]]
														if current_value < next_value:
																					total_value -= current_value
														else:
																					total_value += current_value
														index += 1
							total_value += SYMBOLS[numerals[index]]

							return total_value
def        UpperCAmelCase (    A	:					int					):
							SCREAMING_SNAKE_CASE       :   Any         =      ''''''

							SCREAMING_SNAKE_CASE       :   Dict         =      num // 1000
							numerals += m_count * "M"
							num %= 1000

							SCREAMING_SNAKE_CASE       :   str         =      num // 100
							if c_count == 9:
														numerals += "CM"
														c_count -= 9
							elif c_count == 4:
														numerals += "CD"
														c_count -= 4
							if c_count >= 5:
														numerals += "D"
														c_count -= 5
							numerals += c_count * "C"
							num %= 100

							SCREAMING_SNAKE_CASE       :   Any         =      num // 10
							if x_count == 9:
														numerals += "XC"
														x_count -= 9
							elif x_count == 4:
														numerals += "XL"
														x_count -= 4
							if x_count >= 5:
														numerals += "L"
														x_count -= 5
							numerals += x_count * "X"
							num %= 10

							if num == 9:
														numerals += "IX"
														num -= 9
							elif num == 4:
														numerals += "IV"
														num -= 4
							if num >= 5:
														numerals += "V"
														num -= 5
							numerals += num * "I"

							return numerals
def        UpperCAmelCase (    A	:					str = "/p089_roman.txt"					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      0

							with open(os.path.dirname(A					) + roman_numerals_filename					) as filea:
														SCREAMING_SNAKE_CASE       :   str         =      filea.readlines()

							for line in lines:
														SCREAMING_SNAKE_CASE       :   Any         =      line.strip()
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      parse_roman_numerals(A					)
														SCREAMING_SNAKE_CASE       :   Any         =      generate_roman_numerals(A					)
														savings += len(A					) - len(A					)

							return savings


if __name__ == "__main__":
							print(f'{solution() = }')




